Preferential attachment with partial information

  • Timoteo Carletti
  • Floriana Gargiulo
  • Renaud Lambiotte
Regular Article

Abstract

We propose a preferential attachment model for network growth where new entering nodes have a partial information about the state of the network. Our main result is that the presence of bounded information modifies the degree distribution by introducing an exponential tail, while it preserves a power law behaviour over a finite small range of degrees. On the other hand, unbounded information is sufficient to let the network grow as in the standard Barabási-Albert model. Surprisingly, the latter feature holds true also when the fraction of known nodes goes asymptotically to zero. Analytical results are compared to direct simulations.

Keywords

Statistical and Nonlinear Physics 

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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Timoteo Carletti
    • 1
  • Floriana Gargiulo
    • 1
  • Renaud Lambiotte
    • 1
  1. 1.Department of Mathematics and Namur Center for Complex Systems - naXysUniversity of NamurNamurBelgium

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